Some expert system case histories. “Classic” systems l This refers to the early, pre-1980,...

Some expert system case histories

“Classic” systems

This refers to the early, pre-1980, systems that demonstrated what was possible with the new technology.

They showed that it was possible to capture heuristic knowledge and store

it; make a computer that could dispense

the advice that, previously, only an expert human could provide.

“Classic” systems

As a result, techniques used in these early systems were copied in very many subsequent systems, and in many expert system shells.

Some classic systems

MACSYMA advised the user on how to solve complex

maths problems. DENDRAL

advised the user on how to interpret the output from a mass spectrograph

MYCIN PROSPECTOR R1/XCON

Some influential systems that came later

CENTAUR INTERNIST PUFF CASNET DELTA - locomotive engineering Drilling Advisor - oilfield prospecting ExperTax - tax minimisation advice XSEL - computer sales

All medical expert systems}

A classification of expert system tasks

The following is a classification of expert systems, in terms of the kinds of task that they have been designed to perform.

It was drawn up by Hayes-Roth & colleagues in 1983.


Diagnosis. The process of finding faults in a system, or diseases in a living system.

example: MYCIN - diagnosed blood infection. Shortliffe, 1976.


Interpretation. The analysis of data, to determine their meaning.

example: PROSPECTOR - interpreted geological data as potential evidence for mineral deposits. Duda, Hart, et al 1976.


Monitoring. The continuous interpretation of signals from a system, so that a diagnosis or an alarm can be given when required.

example: NAVEX - monitored radar data and estimated the velocity and position of the space shuttle. Marsh, 1984


Design. The production of specifications from which systems, satisfying particular requirements, can be made.

example: R1/XCON - configured VAX computer systems on the basis of customers' needs. McDermott, 1980.


Planning. The production of a sequence of actions that will achieve a particular goal.

example: MOLGEN - planned chemical processes whose purpose was to analyse and synthesise DNA. Stefik, 1981.


Instruction. Teaching a student a body of knowledge, varying the teaching according to assessments it makes of the student's current knowledge. N.B. This type of expert system is often called an intelligent tutoring system.

example: SOPHIE - instructed the student on the repair of an electronic power-pack. Brown, Burton & de Kleer, 1982.


Prediction. Forecasting future events, using a model based on past events.

example: PLANT - predicted the damage to be expected when a corn crop was invaded by black cutworm. Boulanger, 1983.


Debugging & repair. Generating and, perhaps, administering remedies for system faults.

example: COOKER ADVISER - provides repair advice with respect to canned soup sterilising machines. Texas Instruments, 1986.


Control. Governing the behaviour of a system by anticipating problems, planning solutions, and monitoring actions.

example: VENTILATOR MANAGEMENT ASSISTANT - scrutinised the data from hospital breathing-support machines, and provided accounts of the patients' conditions. Fagan, 1978.

A system that performed diagnosis: MYCIN

Knowledge domain: diagnosing blood infections of the sort that might be contracted in hospital

Written by: Edward Shortliffe and colleagues, 1972 to late 1970s.

MYCIN

Purpose: to assist a physician, who was not an expert in the field of antibiotics, with the diagnosis & treatment of blood disorders (and in particular to establish whether the patient was suffering from a serious infection like meningitis).

Input: symptoms & test results Output: a diagnosis, accompanied by a

degree of certainty, & recommended therapy

MYCIN

Knowledge representation: production rules (and simpler data structures)

Inference engine: Mixed chaining, but principally backward chaining from a top goal: that diagnosis & therapy is needed. Rules are found to satisfy conditions of this rule, then further rules to satisfy these. Evidence may be sought from the user.

Dealing with uncertainty: By calculating certainty factors.

MYCIN

Significance: Much copied in later systems. A successful (and enormously influential)

expert system: did a complex task. performed well: tested against medical students,

non-specialist doctors and blood infection specialists, it did better than the former two groups and equalled the latter group.

but note that MYCIN was just a laboratory demonstration - it was never marketed, or installed in a hospital and used for routine work.

MYCIN

A Consultation with MYCIN@ mycinMYCIN 26-MarchInstructions? (Y or N) N26-March 10:42-----PATIENT-1-----Patient's name: Alan Apt

A Consultation with MYCIN

Age? 34Sex? Why [i.e. Why is it important to

determine the sex of PATIENT-1?]The sex of the patient is one of the

four standard parameters that identify a patient. This may be useful later.


Sex? MaleRace? CaucasianAre there any cultures for Alan

Apt that may be related to the present illness, and from which organisms have been grown successfully in the lab?

No


Are there any pending cultures for Alan Apt?

Yes-----CULTURE-1-----From what site was the specimen for CULTURE-1 taken?

Cerebro Spinal FluidPlease give the date and time when the pending CSF culture was obtained.


26-March-7:00Was a smear examined at the time the specimen was obtained?

YesWere there any cultures seen on the smear of the specimen?

NoAre there any other pending

cultures?


NoHas Alan Apt recently had symptoms of

persistent headache or other abnormal neurological symptoms (dizziness, lethargy, etc)?

YesHas Alan Apt recently had objective

evidence of abnormal neurological signs (nuchal rigidity, coma, seizures, etc) documented by a physician?


YesThe CSF cultures will be

considered to be associated with MENINGITIS.

Please give the date on which clinical evidence (symptoms, signs, or laboratory tests) of the meningitis first appeared.

25 March 10:00Has Alan Apt had neurosurgery?


Why [i.e. WHY is it important to

determine whether PATIENT-1 has had neurosurgery?]

This will aid in determining the organisms (other than those seen on cultures or smears) that might be causing the infection.


It has already been established that [1] the culture that requires therapy is meningitis, and

[2] organisms were not seen on the stain of the pending csf culture (CULTURE-1), and

[3] the type of infection is bacterial


Therefore, if [4] Alan Apt has had neurosurgery,

and [5] the time since the neurosurgical

operation is less than two months, and [6] the type of neurosurgery is not

ventricular-urethal shunt

then there is evidence that the

organisms that might be causing the infection are staphylococcus-coag-pos (0.75), staphylococcus-coag-neg (.5), e.coli (.4), klebsiella (0.3)

Author: Yu [RULE511]


Has Alan Apt had neurosurgery? How [3] [i.e. HOW was it established

that the type of infection is bacterial?]


The following rules concluded about the likelihood that the type of infection is bacterial:

Rule Yes No500 (.4)501 (.17)502 (.1)524 (.42)504 (.28)Thus it has been established that the

type of infection might be bacterial.


Has Alan Apt had neurosurgery? NoIs Alan Apt a burn patient? Yes


I have found evidence (RULE507) that treatment should cover for Diplococcus pneumoniae causing meningitis.

I have found evidence (RULE545) that treatment should cover for e.coli causing meningitis.

I have found evidence (RULE545 RULE578) that treatment should cover for Pseudomonas aeruginosa causing meningitis.


Does Alan Apt have focal neurological signs?

No[Determining which dosages are

desirable for use against the e.coli...]

[Determining which drugs are desirable for use ...]


INFECTION-1 is MENINGITIS+ <ITEM-1> DIPLOCOCCUS-

PNEUMONIAE (from clinical evidence only)

+ <ITEM-2> E.COLI (from clinical evidence only)

+ <ITEM-3> PSEUDOMONAS AERUGINOSA (from clinical evidence only)


[REC-1] My preferred therapy recommendation is as follows:

In order to cover for items <1 2 3>:Give the following in combination:1) AMPICILLIN

Dose: 3.85g(30.8ml) q4h IV

2) GENTAMICINDose: 131mg(3.2ml) q8h IV


Comments: monitor serum concentrations.Since high concentrations of penicillins can inactivate aminoglycosides, do not mix these antibiotics in the same IV bottle.

Do you wish to see the next choice therapy?

No

MYCIN

Description of the system. Written in LISP. MYCIN was a mixed-chaining

production system.

MYCIN

The sequence of operations was that the system asked: Questions to get general details about

the patient (name, age, sex, race, clinical test results already known).

Questions designed to find a possible, general diagnosis (e.g. the patient has probably got some form of meningitis), by forward chaining.

MYCIN

Questions designed to test this theory, and establish specific details, by backward chaining. This was where most of the reasoning was done.

Questions designed to produce a recommended treatment, again by forward chaining.

MYCIN

MYCIN could explain its reasoning in a rather simple way: when asked "Why do you think that is

the diagnosis?”, MYCIN listed the rules it had applied, in reverse order, with CFs.

When asked "Why do you want to know that?", MYCIN described the rule it was trying to execute, and what value it was trying to find.

A system that performed prescription: CROP ADVISOR

Developed by ICI (in 1989) to advise cereal grain farmers on appropriate fertilisers and pesticides for their farms.

The choice of chemical, amount, and time of application depends on such factors as crop to be grown, previous cropping, soil condition, acidity of soil, and weather.

Farmers can access the system via the internet.

CROP ADVISOR

Given relevant data, the system produces various financial return projections for different application rates of different chemicals.

The system uses statistical reasoning to come to these conclusions.

If the question asked is outside the system's expertise, it refers the caller to a human expert.

CROP ADVISOR

The chief advantages of this system have been that employees at ICI have been

relieved of the need to provide lengthy telephone advice sessions,

and the quality of the advice has become much more uniform, which has increased confidence in the company's products.

A system that performed configuration/design:

R1/XCON

Knowledge domain: Configuring VAX computers, to customers' specifications.

Written by: John McDermott and colleagues, 1978 - 1981

Input: Required characteristics of the computer system.

Output: Specification for the computer system.

R1/XCON

Knowledge representation: Production rules.

Inference engine: Forward chaining: the output specification was assembled in working memory.

Dealing with uncertainty: No mechanism for this: the system simply assembled one answer, assumed to be good enough to do the job.

R1/XCON

Significance:

A rather simple forward-chaining rule-based expert system, which nevertheless performed well, solved a difficult manufacturing problem, and proved to be enormously profitable.

R1/XCON

Digital Equipment Corporation's problem was that they were marketing the best-selling Vax-11 series of computers, and the department responsible for configuration was failing to keep up with customer demand.

R1/XCON

Each computer was the result of a consultation between a sales executive and the customer, designed to discover the customer's requirements, after which a configuration was drawn up, from which the system was built.

R1/XCON

Each configuration was taking 25 minutes, and orders were arriving at a rate of 10,000 a year.

There was a high level of errors in the configurations produced.

R1/XCON

DEC attempted to write a conventional program to do this task, with no success, then invited McDermott to write an AI system to do it. McDermott wrote R1/XCON.

By 1986, it had processed 80,000 orders, and achieved 95-98% accuracy. It was reckoned to be saving DEC $25M a year.

R1/XCON

However, R1/XCON suffered from the shortcomings of simple production-rule-based systems. When the nature of the task changed,

fresh rules were simply added at the end of the rulebase.

Soon, the rulebase was very large, unreliable and incomprehensible.

Expensive rewriting was needed to restore the operation of the system.

A system that performs planning: OPTIMUM-AIV

OPTIMUM-AIV is a planner used by the European Space Agency (1994) to help in the assembly, integration, and verification of spacecraft.

It generates plans, and monitors their execution.

OPTIMUM-AIV

Unlike a conventional scheduling tool, it has a knowledgebase which describes the underlying causal links that dictate that the assembly must be undertaken in a particular order.

Therefore, if a plan fails and has to be repaired, the system can make intelligent decisions about which alternative plans will work and which will not.

OPTIMUM-AIV

It can engage in hierarchical planning - this involves producing a top-level plan with very little detail, and then turning this into increasingly more detailed lower-level plans.

It can reason about complex conditions, time, and resources (such as budget constraints).

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Some expert system case histories. “Classic” systems l This refers to the early, pre-1980,...

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